A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.

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Bibliographic Details
Title: A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.
Authors: Hong J; School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China., Chen C; Department of Radiology, Fujian Maternal and Child Hospital, Fujian Key Laboratory of Women and Children's Critical Diseases Research, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China., Li M; School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China., Qiu J; School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China., Dong B; Department of Radiology, Fujian Maternal and Child Hospital, Fujian Key Laboratory of Women and Children's Critical Diseases Research, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China., Lin Y; School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China. yplin@xmut.edu.cn.
Source: Biomedical engineering online [Biomed Eng Online] 2026 Feb 20; Vol. 25 (1). Date of Electronic Publication: 2026 Feb 20.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101147518 Publication Model: Electronic Cited Medium: Internet ISSN: 1475-925X (Electronic) Linking ISSN: 1475925X NLM ISO Abbreviation: Biomed Eng Online Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1475-925X
DOI:10.1186/s12938-026-01518-5